We present, in this short paper, a model of artificial
brain based on the Software-Hardware integration
in the "1 + 1 = 1" philosophy framework using machine
learning and multiprocessor system on chip, SoC.
Its virtual experiences are generated by a deep learning
process with random changing of the structure of
a net of artificial neural network, NoNN, using Monte
Carlo method. It ensures creative property of the human
cognitive processing and possibility of the "humanmachine"
integration/"Human brain-Artificial Brain" integration,
which should be applied in various areas of
online control.

“1 + 1 = 1” !
According to Georg Wilhelm Friedrich Hegel (1770-1831), we
cannot have the "pure being" unless we also take into consideration
"pure nothing" i.e., being and nothing are oneness (1 +
1 = 1) which is neither being nor nothing but a third option
presented rather by meta-language, "being-nothing", that transcends
both. It we call the "one" philosophy. In fact, the "one"
philosophy is find in Indian four-cornered argumentation logic
(about 500 BC): affirmation X, negation of X, ¬X, both X ∧ ¬X
and neither ¬(X∨ ¬X), in which, X ∧ ¬X is integration of X
and ¬X which represents a third option that transcends both. It
describers not knowledge originated from logic (logical knowledge)
but rather knowledge originated from a cognitive process
in the human brain; in the Chinese "one" philosophy presented
by Zhuang Zhou - (369-286 B.C.), in which he claimed that Discrimination,
and the opposition of it, Nondiscrimination, are seen
as oneness Discrimination-Nondiscrimination. It is not the common
sum, Discrimination + Nondiscrimination, but rather an integration
described perhaps firstly in the world by meta-language
Discrimination-Nondiscrimination referred as a third option that
transcends both Discrimination and Nondiscrimination. It represents
his feeling comes from cognitive processing; in Levis’s modal
logic and Lukasiewicz’s multivalued logic (1930), in which, the
value of truth like the third argumentation of Indian logic, is
the integration of true and false - "false-false" integration. It is
finding also in the "membership-nonmembership" integration presented
in the Lotfi Zadeh’s fuzzy set theory (1965). It is clear
that various meta-concepts, "being-nothing", "Discrimination-
Nondiscrimination", "true-false" and "membership-nonmembership"
have not meaning associated with common word/words, but another
meaning related to the denotative concept described in the
"sense and denotation" theory written by Frege FLG (1848-1925),
which represents a meaning in the semantic and pragmatic aspects
of both conscious and non-conscious thoughts of the human
brain [7].

Likewise, according to the "one" philosophy, it is impossible
to understand the real world (reality) without the virtual world
(virtual reality) contained in mind (in the human brain). That
is, when we receive what we consider to be reality, which is
a limit to what can be known of the so called objective reality.
It is a first perception, which we can describe through common
language and Boolean numbers within the syntax constraints
and counter-concepts contained in the framework of two-valued
logic. We have also relationships to this reality through our brain
with chemical, energetic responses and responses of our organsenses.
That is to say, we have relationships to other part of reality
through high level cognitive responses. It is strictly limited
to brain based processing related to an interface for another part
of reality. What we get from there, through intuition, experience
and process of cognitive information processing, is called second
perception. Its description is, sometime, beyond limits of common
language and Boolean numbers. This second perception described
by meta- language represents the semantic and pragmatic
respects of new knowledge in the framework of epistemology including
predictive mathematics and modern logic. Thus, conception of reality depends really on how to create also a simulation
of the cognition and cognitive processing. We are in the state
of transitioning to an increasingly mind-dependent viewpoint of
reality which represents a necessary integration of the real world
and the human brain (the virtual world) - the "reality-virtual reality"
integration rather than common connection "reality + virtual
reality". It is really the human participation to deal, in the "one"
philosophy framework with the "Human-Machine" integration for
various fields of online control.

The Real World and Virtual World

The Internet of Things, IoT, has evolved from the convergence
of the Internet, wireless technologies and Micro-Electro-
Mechanical Systems, MEMS, which contains micro-circuitry on a
tiny silicon chip into which some mechanical device such as a sensor
has been manufactured. Wireless is a term used to describe
telecommunications in which electromagnetic waves/acoustic
waves carry the signal over part or the entire communication path
called machine to machine, M2M, communication. Generally, the
physical signals from sensor are sent to an analog signal processing
device in the form of an amplifier or a low-pass filter. On the
basis of the information received, the actuators make the necessary
decisions about how to respond to the appropriate actions. It
creates, however, a new type of data, big data, James [9], Vossen
G [19] - a large number of datasets in the streaming and dynamic
forms. Thus, we need a new approach that seeks to discover new
information, identify and categorize data, focusing on exploring
natural phenomena, acquiring new knowledge, and understanding
real-time laws of nature. IoT is an environment where the real
world and virtual world are constantly connected via the wireless
sensor network, WSN, in which, internet/cloud is referred to as
the virtual world. It enables us to observe and control our surroundings
anytime, anywhere, which is represented graphically
in the following figure 1.

On the one hand, the real world is really connected to the human
brain (the virtual world) through the organ senses. Thus,
the human brain becomes the Brain of Thing, BoT, (the intelligent
IoT, iIoT), which is shown in the following figure 2.

On the other hand, the human brain or brain in short term
(B) is still a physical world. It has a virtual world, which should

Figure 2:The Brain of Thing, BoT.

be an Artificial Brain/Informatics Brain (AB/IB). Thus, we have
like the Internet of Thing the "B (real world) AB (virtual world)"
connection. Collected information about the states of the human
brain through brainwaves is transmitted to the artificial brain using
brain wearable in order to detect the human thoughts, feelings
regardless of the human behavior and to monitor controller’s
experiences, detect focused thoughts of the human brain [20].
It is not the "B-AB" connection in the common sense but rather
the "B-AB" integration in the "1 + 1 =1 philosophy" framework,
which is so called "Human-Machine" integration. It is neither Human
nor Machine but a third option - high level intelligence which
is presented in the following figure 3.

Figure 3:The "Brain-Artificial Brain" integration.

It should be used to control physical electronics with mere
thoughts and to expand and improve the way our brains is studied
and understood.

The Artificial Brain (AB), has rather artificial experiences created
by learning process using virtual training data and modern
informatics in short time. It should be constructed to deal
with big data analysis, to support unpredictable queries against
cognitive data, to analyze the so-called stream dynamical data
in order to discover new insights focused on investigating natural
phenomena, acquiring new knowledge, and understanding
the laws of nature. To do this, we use Machine Learning (ML)
as a software part and MultiProcessorSystem on a chip/system
on a chip, SoC, as a hardware component to build an IT model
as a software-hardware integration within the "one" philosophy
framework, which is presented on the following figure 4.

Figure 4:The "Brain-Artificial Brain" integration.

Hardware

MultiProcessorSystem on Chip, SoC. Instead of CPU architecture,
SoC is a microchip with all necessary electronic circuits and
parts for a given system on a single Integrated Circuit, IC - a chip,
or a microchip, which includes a Reduced Instruction Set Computer
(RISC) [10]. The SoC architecture is specified as a set of
processor and hardware subsystems that interact via communication
network referred to as Net on Chip, NoC.

The open multi-processing, OpenMP, is a parallel programming
model for SoC, a programming interface that supports
multiplatform shared memory multiprocessing programming. It
assumes a shared memory model with all the threads having
access to the same globally shared memory. The OpenMP consists
of a set of compiler directives to define a parallel region. We
can write an OpenMP program by inserting OpenMP directives
(parallel loop, work is distributed) into a serial program, for
example,

#programa omp parallel for
for(i = 0;i < 100; i++)
{
Data[i] = 0;
}

Cognitive Chip, CC. The SoC technology is developed using
neurosynaptic chip/Cognitive Chip (CC) or a microprocessor that
functions like more the human brain than a common CPU does. It
is made to function like the human brain on the hardware rather
than software level used to imitate thoughts and learn of the human
brain. IBM’s brain-inspired architecture consists of a network
of neurosynaptic cores, which are distributed and operate
in parallel. According to IBM’s study, a chip of one million neuron
brain-inspired processor is capable of 46 billion synaptic operations
per second.

IBM’s Supercomputer. Researches from IBM create computing
system - new supercomputer used to simulate and emulate the
brain’s abilities for sensation, perception, action, interaction and
cognition with compact size. The new supercomputer, according
to IBM research lab, will consist of 64 million neurons and
16 billion synapses. A single processor in the system consists of
5.4 billion transistors organized into 4096 neural cores, creating
an array of 1 million digital neurons that communicate with one
another via 256 million electrical synapses. The architecture is
specified as a set of cores (processors) and hardware subsystems
that interact via communication network including local instruction
and data bus.

Software

The parallelization mechanism is called partitioning, which will
be mapped on the target architecture. The parallelization of the
application consists in dividing the computation in several pieces
that can be executed in parallel. These different pieces group
several functions of the application are named tasks or processes.
The mapping represents the association between the tasks and the
processing elements on which they are executing, and the association
between the buffers used for the communication between the
tasks and the hardware. For example, we can use the analysis of
big data through Apache’s Hadoop using Google’s MapReduce. It,
in particular, the Hadoop Distributed File System (HDFS) is data
storage component of the open source Apache’s Hadoop project
which had evolved for excellent offline data processing platform
[18,21].

Software based MapReduce is a programming model and the
Google’s software framework for large-scale distributed computing
on large amounts of data in order to collect and to mine,
e.g., satellite and thermal imagery, and other readily available
information. Given a set of data, a series of operations (kernel
functions) is applied to each element in the dataset and local onchip
memory is reused to minimize external memory bandwidth
and to automate and optimize on-chip management "map-reduce"
process.

Machine Learning provides computers with an ability to learn
without programming. Various approaches are being taken to
formulate a new field of so-called machine learning. According
to Corinna Cortes, et al. [4], a new type of machine learning
(support-vector network) is constructed. It implements the following
idea: mapping the input vectors into some high dimensional
feature space Z through some non-linear mapping chosen
a priori, constructing a linear decision surface with special properties
that ensure high generalization ability of the network. Neural
networks are a common type of another Machine Learning, ML,
that tries to mimic, how neurons interrelate in the human brain
to pass information around. An attempt to emphasize the neurobiological
aspects of artificial neural computation is presented
by different authors, [2,3,5]. It focuses on the development of
an artificial brain that can learn to develop own experience and
change under the influence of new data. It can be used to detect
patterns in data and adjusts program actions accordingly. ML
not only enables us to understand the notion of the real world,
but enables us to learn and change its own future behavior also.
Also, how we can overcome unwanted situations while building
NN models is presented by Tavish Srivastava [17].

We present in this paper a combination, according to "SW-HW"
integration, of a net of multilayer feedforward neural networks
FFNN, with sigmoidal neurons, and SoC used for simulation of
an inductive learning process[14]. In which, we use the Resilient
Back Propagation, RBP, training algorithm in order to eliminate
the harmful effects of the magnitudes of the partial derivatives.

Training data is the one problem of machine learning, in
which we have to generate sufficient enough of a number of training
data with suitable distribution within the problem domain.
Before we start learning, we need a lot of training data, but we
usually do not have all the data available to train the computer for
learning. Training data has a significant effect on the learning and
generalization performance of a network. Depending on studied
problem, for the "human-machine" integration for example, training
data set is created by an available benchmark database of
EEG data labeled with emotions. While in the structural mechanics,
training data/virtual data is created by integration of Monte
Carlo Simulation and Finite Element Method (MCSFEM). We perform,
by this way, a combination of highly creative mathematical
possibilities of Monte Carlo simulation and high accuracy in the
mechanical aspects of FEM.

Least Squares Error, LSE, is an example of supervised training,
in which the learning rule is provided with a set of examples, n
inputs, pn, and n target outputs, tn, to the network of desired
behavior:
{p1, t1}, {p2, t2}, …, {pn, tn} (1)MathType@MTEF@5@5@+=
feaagGart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn
hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr
4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9
vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x
fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaae4Eaiaabc
hadaWgaaWcbaGaaeymaaqabaGccaqGSaGaaeiiaiaabshadaWgaaWc
baGaaeymaaqabaGccaqG9bGaaeilaiaabccacaqG7bGaaeiCamaaBa
aaleaacaqGYaaabeaakiaabYcacaqGGaGaaeiDamaaBaaaleaacaqG
Yaaabeaakiaab2hacaqGSaGaaeiiaiabgAci8kaabYcacaqGGaGaae
4EaiaabchadaWgaaWcbaGaaeOBaaqabaGccaqGSaGaaeiiaiaabsha
daWgaaWcbaGaaeOBaaqabaGccaqG9bGaaeiiaiaabccacaqGGaGaae
iiaiaabccacaqGGaGaaeiiaiaabIcacaqGXaGaaeykaaaa@579F@
The error is calculated as the difference between the target output,
t, and the network output, a, in order to minimize the average
of the sum of these errors:
MSE=1N∑n=1Ne(n)2=1N∑n=1N(t(n)−a(n))2 (2)MathType@MTEF@5@5@+=
feaagGart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn
hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr
4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9
vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x
fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamytaiaado
facaWGfbGaeyypa0ZaaSaaaeaacaaIXaaabaGaamOtaaaadaaeWbqa
aiaadwgacaGGOaGaamOBaiaacMcadaahaaWcbeqaaiaaikdaaaGccq
GH9aqpaSqaaiaad6gacqGH9aqpcaaIXaaabaGaamOtaaqdcqGHris5
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aiaacMcadaahaaWcbeqaaiaaikdaaaaabaGaamOBaiabg2da9iaaig
daaeaacaWGobaaniabggHiLdGccaqGGaGaaeiiaiaabccacaqGGaGa
aeiiaiaabccacaqGGaGaaeikaiaabkdacaqGPaaaaa@5D9C@
The LSE algorithm adjusts the weights and biases of the linear
network so as to minimize this mean square error, MSE.

Support Vector Machine, SVM, have been introduced for solving
pattern recognition problems. JAK Suykens, J Vandewalle
[16] formulate a least squares version of support vector machine,
SVM, for classification problems with two classes. It is based
on Vapnik-Chervonenkis theory and structural risk minimization
principle. Authors claimed that a least squares SVM with Gaussian
radial basis function, RBF, kernel is readily found with excellent
generalization performance and low computational cost.
Many algorithms are introduced, which are employed to classify
the data into emotions, see Lin et al. [11] or Murugappan et
al. [13]. It indicates that we can create an ability to recognize
the human emotions from elektroencefalografia, (EEG) which is
a non-invasive diagnostic method used to study bioelectrical brain
activity from the scalp in order to approximate a level of the human
brain intelligence.

Resilient back propagation (RBP) training presented by Martin
Riedmiller et al. [12], can be used in order to eliminate harmful
effects of the magnitudes of the partial derivatives in order to
overcome the inherent disadvantages of pure gradient-descent.
Only the sign of the derivative is used to determine the direction
of the weight update. Size of the weight change is determined
by a separate update value. Authors introduce for each weight
its individual update-value, Δij, which solely determines the size
of the weight-update. This adaptive update-value evolves during
the learning process based on its local sight on the error function
E, according to the following learning rule:
Δij(t)={η+.Δij(t−1),if∂E(t−1)∂wij.∂E(t)∂wij>0η−.Δij(t−1),if∂E(t−1)∂wij.∂E(t)∂wij<0Δij(t−1),else (10)MathType@MTEF@5@5@+=
feaagGart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn
hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr
4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9
vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x
fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaeuiLdq0aa0
baaSqaaiaadMgacaWGQbaabaGaaiikaiaadshacaGGPaaaaOGaeyyp
a0ZaaiqaaeaafaqabeWabaaabaGaeq4TdG2aaWbaaSqabeaacqGHRa
WkaaGccaGGUaGaeuiLdq0aa0baaSqaaiaadMgacaWGQbaabaGaaiik
aiaadshacqGHsislcaaIXaGaaiykaaaakiaacYcadaahaaWcbeqaaa
aakiaadMgacaWGMbWaaSaaaeaacqGHciITcaWGfbWaaWbaaSqabeaa
caGGOaGaamiDaiabgkHiTiaaigdacaGGPaaaaaGcbaGaeyOaIyRaam
4DamaaBaaaleaacaWGPbGaamOAaaqabaaaaOGaaiOlamaalaaabaGa
eyOaIyRaamyramaaCaaaleqabaGaaiikaiaadshacaGGPaaaaaGcba
GaeyOaIyRaam4DamaaBaaaleaacaWGPbGaamOAaaqabaaaaOGaeyOp
a4JaaGimaaqaaiabeE7aOnaaCaaaleqabaGaeyOeI0caaOGaaiOlai
abfs5aenaaDaaaleaacaWGPbGaamOAaaqaaiaacIcacaWG0bGaeyOe
I0IaaGymaiaacMcaaaGccaGGSaWaaWbaaSqabeaaaaGccaWGPbGaam
OzamaalaaabaGaeyOaIyRaamyramaaCaaaleqabaGaaiikaiaadsha
cqGHsislcaaIXaGaaiykaaaaaOqaaiabgkGi2kaadEhadaWgaaWcba
GaamyAaiaadQgaaeqaaaaakiaac6cadaWcaaqaaiabgkGi2kaadwea
daahaaWcbeqaaiaacIcacaWG0bGaaiykaaaaaOqaaiabgkGi2kaadE
hadaWgaaWcbaGaamyAaiaadQgaaeqaaaaakiabgYda8iaaicdaaeaa
cqqHuoardaqhaaWcbaGaamyAaiaadQgaaeaacaGGOaGaamiDaiabgk
HiTiaaigdacaGGPaaaaOGaaiilamaaCaaaleqabaaaaOGaamyzaiaa
dYgacaWGZbGaamyzaaaaaiaawUhaaiaabccacaqGGaGaaeiiaiaabc
cacaqGGaGaaeiiaiaabccacaqGGaGaaeikaiaabgdacaqGWaGaaeyk
aaaa@9A6B@
Where 0 < η- < 1 < η+ . Once the update-value for each
weight is adapted, the weight-update it follows a simple rule:
Δwijt={−Δij(t),if∂E(t)∂wij>0+Δij(t),if∂E(t)∂wij<00,else;wij(t+1)=wij(t)+Δwij(t) (11)MathType@MTEF@5@5@+=
feaagGart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn
hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr
4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9
vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x
fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaeuiLdqKaam
4DamaaDaaaleaacaWGPbGaamOAaaqaaiaadshaaaGccqGH9aqpdaGa
baqaauaabeqadeaaaeaacqGHsislcqqHuoardaqhaaWcbaGaamyAai
aadQgaaeaacaGGOaGaamiDaiaacMcaaaGccaGGSaWaaSbaaSqaaaqa
baGccaWGPbGaamOzamaalaaabaGaeyOaIyRaamyramaaCaaaleqaba
GaaiikaiaadshacaGGPaaaaaGcbaGaeyOaIyRaam4DamaaBaaaleaa
caWGPbGaamOAaaqabaaaaOGaeyOpa4JaaGimaaqaaiabgUcaRiabfs
5aenaaDaaaleaacaWGPbGaamOAaaqaaiaacIcacaWG0bGaaiykaaaa
kiaacYcadaWgaaWcbaaabeaakiaadMgacaWGMbWaaSaaaeaacqGHci
ITcaWGfbWaaWbaaSqabeaacaGGOaGaamiDaiaacMcaaaaakeaacqGH
ciITcaWG3bWaaSbaaSqaaiaadMgacaWGQbaabeaaaaGccqGH8aapca
aIWaaabaGaaGimaiaacYcadaahaaWcbeqaaaaakiaadwgacaWGSbGa
am4CaiaadwgaaaaacaGL7baacaGG7aWaaSbaaSqaaaqabaGccaWG3b
Waa0baaSqaaiaadMgacaWGQbaabaGaaiikaiaadshacqGHRaWkcaaI
XaGaaiykaaaakiabg2da9iaadEhadaqhaaWcbaGaamyAaiaadQgaae
aacaGGOaGaamiDaiaacMcaaaGccqGHRaWkcqqHuoarcaWG3bWaa0ba
aSqaaiaadMgacaWGQbaabaGaaiikaiaadshacaGGPaaaaOGaaeiiai
aabccacaqGGaGaaeiiaiaabccacaqGGaGaaeiiaiaabccacaqGOaGa
aeymaiaabgdacaqGPaaaaa@89AF@
With one exception:
Δwij(t)=−Δwij(t−1),if∂E(t−1)∂wij.∂E(t)∂wij<0 (12)MathType@MTEF@5@5@+=
feaagGart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn
hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr
4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9
vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x
fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaeuiLdqKaam
4DamaaDaaaleaacaWGPbGaamOAaaqaaiaacIcacaWG0bGaaiykaaaa
kiabg2da9iabgkHiTiabfs5aejaadEhadaqhaaWcbaGaamyAaiaadQ
gaaeaacaGGOaGaamiDaiabgkHiTiaaigdacaGGPaaaaOGaaiilamaa
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ahaaWcbeqaaiaacIcacaWG0bGaeyOeI0IaaGymaiaacMcaaaaakeaa
cqGHciITcaWG3bWaaSbaaSqaaiaadMgacaWGQbaabeaaaaGccaGGUa
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aaaaGccqGH8aapcaaIWaGaaeiiaiaabccacaqGGaGaaeiiaiaabcca
caqGGaGaaeikaiaabgdacaqGYaGaaeykaaaa@6706@
Using, in this paper, two keener functions: Hyperbolic tangent
sigmoid, respectively:
tansig(n)=2/1(1+exp(−2*n))−1; logsig(n)=1/(1+exp(-n)), (13)MathType@MTEF@5@5@+=
feaagGart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn
hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr
4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9
vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x
fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaaeiDaiaabg
gacaqGUbGaae4CaiaabMgacaqGNbGaaeikaiaab6gacaqGPaGaeyyp
a0JaaGOmaiaac+cacaaIXaGaaiikaiaaigdacqGHRaWkciGGLbGaai
iEaiaacchacaGGOaGaeyOeI0IaaGOmaiaacQcacaqGUbGaaiykaiaa
cMcacqGHsislcaaIXaGaai4oaiaabccacaqGSbGaae4BaiaabEgaca
qGZbGaaeyAaiaabEgacaqGOaGaaeOBaiaabMcacaqG9aGaaeymaiaa
b+cacaqGOaGaaeymaiaabUcacaqGLbGaaeiEaiaabchacaqGOaGaae
ylaiaab6gacaqGPaGaaeykaiaabYcacaqGGaGaaeiiaiaabccacaqG
GaGaaeiiaiaabccacaqGOaGaaeymaiaabodacaqGPaaaaa@684F@
This allows for all weights of the neural network to adapt in order
to minimize the error on a set of vectors belonging to pattern
recognition problems.

Software-Hardware Integration

In fact, SW and HW are undoubtedly more sophisticated than
some years ago. The SW-HW integration, in deep idea of the
"1 + 1 = 1"philosophy, is what far more than the sum, SW +
HW, separately. It creates (1) the so called MapReduce based
on "SW-HW" integration rather than the SW based MapReduce;
(2) Learning based on "SW-HW"integration which are presented
bellows.

MapReduce based on "SW-HW"integration is new form using
SoC rather than SW based MapReduce. It will parse the input,
splits to each slave (core of SoC) and produces records. Intermediate
results generated in the map phase are sorted by MapReduce,
entered into slaves and next into the reduction phase. This
MapReduce connected with program openMP is shown in the following
figure 5.

Figure 5:The "Brain-Artificial Brain" integration.

Learning based on "SW-HW"integration. Multilayer neural
networks can have several layers in all cases, but it is not obvious
how many neural layers and neuron numbers of each to include in
the network. Generally, NN consists of interconnected processing
elements called nodes or neurons that work together to produce
an output function. We have NN architecture presented by: pi -
mj - ok - Wj; i ϵ I (input number), j ϵ J (layer number); k ϵ K
(output number); where, pi, denotes input data; mj ϵ M (neuron
number in each layer); ok - output number; Wj denotes weigh.
The teaching of the human brain is represented by continuous
changes in the parameters p, m, o, w, in the permissible spaces I,
J, K, W respectively of the thought function to achieve any goals:
Thought=∀i∈I,j∈J,k∈K,w∈Wf(pi,mj,ok,Wj) (14)MathType@MTEF@5@5@+=
feaagGart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn
hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr
4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9
vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x
fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamivaiaadI
gacaWGVbGaamyDaiaadEgacaWGObGaamiDaiabg2da9iabgcGiImaa
BaaaleaacaWGPbGaeyicI4SaamysaiaacYcacaWGQbGaeyicI4Saam
OsaiaacYcacaWGRbGaeyicI4Saam4saiaacYcacaWG3bGaeyicI4Sa
am4vaaqabaGccaWGMbWaaeWaaeaacaWGWbWaaSbaaSqaaiaadMgaae
qaaOGaaiilaiaad2gadaWgaaWcbaGaamOAaaqabaGccaGGSaGaam4B
amaaBaaaleaacaWGRbaabeaakiaacYcacaWGxbWaaSbaaSqaaiaadQ
gaaeqaaaGccaGLOaGaayzkaaGaaeiiaiaabccacaqGGaGaaeiiaiaa
bccacaqGGaGaaeiiaiaabIcacaqGXaGaaeinaiaabMcaaaa@61BF@
Changes of these arguments in the random way ensure the creativity
of the human unconscious thought that provides not only
experience but also special capability of the human brain such as
creation and intuition. For example, the artificial experience is
generated by a learning process in short time using virtual training
data. It is originated from a number of NN, connected by
net on chip, NoC, which we call a net of NN, NoNN. Two neural
layers for feedforward network are accepted and large number of
bigdata processing are used to search an optimal NN structure,
which is formulated such that during training using SIMD technology,
neural network structure is changing continuously and
the weights of the network are iteratively adjusted in order to
minimize the network performance function, MSE < 5*10-6 [8].
For layered feedforward network with equality: input number =
output number = number of neurons of second layer = q; m
denotes neuron number of first layer; M denotes the acceptable
neurons number (a fixed number of first layer, M=50 for example);
j denotes layer number, j ϵ L; n - a number of slaves, n ϵ N
(NN number). Then, q-m-q-q is referred as NN architecture. An
optimal solution of the NoNN is presented by:
Min(MSE)=∃q−m−q−qMinn∈N{minm∈M,j∈L(MSEj)1,minm∈M,j∈L(MSEj)2,...,minm∈M,j∈L(MSEj)n} (15)MathType@MTEF@5@5@+=
feaagGart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn
hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr
4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9
vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x
fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqaaiaad2eaca
WGPbGaamOBaiaacIcacaWGnbGaam4uaiaadweacaGGPaGaeyypa0Ja
ey4aIqYaaSbaaSqaaiaadghacqGHsislcaWGTbGaeyOeI0IaamyCai
abgkHiTiaadghaaeqaaOWaaybuaeqaleaacaWGUbGaeyicI4SaamOt
aaqab0qaaiaad2eacaWGPbGaamOBaaaakmaacmaabaWaaybuaeqale
aacaWGTbGaeyicI4SaamytaiaacYcacaWGQbGaeyicI4Saamitaaqa
b0qaaiGac2gacaGGPbGaaiOBaaaakmaabmaabaGaamytaiaadofaca
WGfbWaaSbaaSqaaiaadQgaaeqaaaGccaGLOaGaayzkaaWaaSbaaSqa
aiaaigdaaeqaaOGaaiilamaawafabeWcbaGaamyBaiabgIGiolaad2
eacaGGSaGaamOAaiabgIGiolaadYeaaeqaneaaciGGTbGaaiyAaiaa
c6gaaaGcdaqadaqaaiaad2eacaWGtbGaamyramaaBaaaleaacaWGQb
aabeaaaOGaayjkaiaawMcaamaaBaaaleaacaaIYaaabeaakiaacYca
caGGUaGaaiOlaiaac6cacaGGSaWaaybuaeqaleaacaWGTbGaeyicI4
SaamytaiaacYcacaWGQbGaeyicI4Saamitaaqab0qaaiGac2gacaGG
PbGaaiOBaaaakmaabmaabaGaamytaiaadofacaWGfbWaaSbaaSqaai
aadQgaaeqaaaGccaGLOaGaayzkaaWaaSbaaSqaaiaad6gaaeqaaaGc
caGL7bGaayzFaaaacaqGGaGaaeiiaiaabccacaqGGaGaaeiiaiaabc
cacaqGGaGaaeiiaiaabIcacaqGXaGaaeynaiaabMcaaaa@8C31@
Search of optimal architecture (good enough) is shown in the
figures 6a,b.

Machine Learning is essentially a system of machines outfitted
with data-collecting technologies so that those machines can communicate with one another.

Figure 6a:Deep learning of NoNN based on the "HW-SW"
integration.

Figure 6b:To make an optimal decision in the framework of the
"HW-SW" integration.

ML functions in the SoC, in which,
different NNs are distributed in different cores connected by net
on chip, NoC, referred to as interface. In this paper, we want to
make a learning program as flexible as possible with respect to
the configuration of the SoC architecture, including n = 8 processors
referred as 8 slaves. Determining the optimal architecture
of NN through deep learning using NoNN and the integration of
Monte Carlo and Finite Element Method, MCSFEM, based on the
"SW-HW" integration is shown in the following figure 7.

Figure 7:Machine Learning based on the platform of the System
on Chip, SoC.

It should be noted that the training of CC and IBM supercomputers
will increase processing speed and significantly increase the changing possibility of the number of NN layers as well as the
number of neurons in each layer and values of weights, resulting
in more accurate and more creative effects.

The "Bridge-Car" Talking Application. For example, the structures
equipped with sensors to its monitor in order to communicate
the information via the wireless internet to cars. This connection
from sensors connected directly to internet/cloud, and then
to actuator of the car through the wireless internet, turns critical
information to make decision.

Figure 8:Machine Learning based on the platform of the System
on Chip, SoC.

"Human Brain-Artificial Brain" Integration

As you see, the human brain, B, has not computational capability
for big data as the artificial brain, while the artificial brain has
no capability to deal with uncertainties as the human brain can
do. We need the human participation for cognitive information
processing, which creates a new field of the so called humanmachine
integration. It is interaction-based systems - an open
challenge of online control.

Human-Machine Integration Application. A new idea of
PUMA 560 robot control, through concentration of thoughts, using
the combination of wireless EEG devices and the artificial
brain containing the virtual experiences after training, is shown
in the figure 9.

Figure 9:Machine Learning based on the platform of the System
on Chip, SoC.

It represents the Human-Machine integration (the "B-AB" integration)
using ML based on the platform of the SoCC (System on
Cognitive Chip).

Conclusions and Discussion

It indicates that human intelligence is based on not only human
experiences derived from many years of live but also analysis
in real time of new event, based on random changes of the NN architecture
itself without our consciousness - It works on itself. It
is formulated using a deep learning process, in which, an intelligent
decision will be reached by machine learning and random
changes continuously of NN architecture of a net of neural network
NoNN.

Finely, the "B-AB" integration helps us to get the so called high
level intelligence, which should be applied in new areas of online
control and others.

Work in the future. Because the human brain not only collects
and analyzes information from the senses passively, but also
actively introduces without conscious reasoning. the hypothesis
used to control the sensory activity reversely. It is special capability
of the human brain which we call the human intuition, creativity.
By the way presented above utilizing intuitive logic and
IBM’s supercomputer, we would like to develop a computational
model used to find full solutions of the thought function in order
to mimic those brain abilities.